


import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
__magic_name__  	=     logging.get_logger(__name__)

__magic_name__  	=     {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn.grep_linear''': '''encoder.layers.*.attention.gru_rel_pos_linear''',
    '''self_attn.relative_attention_bias''': '''encoder.layers.*.attention.rel_attn_embed''',
    '''self_attn.grep_a''': '''encoder.layers.*.attention.gru_rel_pos_const''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
__magic_name__  	=     [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]







def 							__magic_name__    (     lowerCAmelCase_     ,	lowerCAmelCase_     ,	lowerCAmelCase_     ,	lowerCAmelCase_     ,	lowerCAmelCase_):
	'''simple docstring'''


	for attribute in key.split("."):
		lowerCamelCase_					:				Optional[Any]              =  getattr(a_     ,	a_)

	if weight_type is not None:
		lowerCamelCase_					:				List[str]              =  getattr(a_     ,	a_).shape
	else:
		lowerCamelCase_					:				Dict              =  hf_pointer.shape

	assert hf_shape == value.shape, (
	    F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
	    F""" {value.shape} for {full_name}"""
	)

	if weight_type == "weight":
		lowerCamelCase_					:				str              =  value
	elif weight_type == "weight_g":
		lowerCamelCase_					:				Union[str, Any]              =  value
	elif weight_type == "weight_v":
		lowerCamelCase_					:				Dict              =  value
	elif weight_type == "bias":
		lowerCamelCase_					:				Union[str, Any]              =  value
	else:
		lowerCamelCase_					:				Optional[int]              =  value

	logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""")







def 							__magic_name__    (     lowerCAmelCase_     ,	lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_					:				int              =  []
	lowerCamelCase_					:				Union[str, Any]              =  fairseq_model.state_dict()

	lowerCamelCase_					:				List[str]              =  hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		lowerCamelCase_					:				Tuple              =  False
		if "conv_layers" in name:
			load_conv_layer(
			    a_     ,	a_     ,	a_     ,	a_     ,	hf_model.config.feat_extract_norm == "group"     ,	)
			lowerCamelCase_					:				Union[str, Any]              =  True
		else:
			for key, mapped_key in MAPPING.items():
				if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
					lowerCamelCase_					:				List[Any]              =  True
					if "*" in mapped_key:
						lowerCamelCase_					:				int              =  name.split(a_)[0].split(".")[-2]
						lowerCamelCase_					:				str              =  mapped_key.replace("*"     ,	a_)
					if "weight_g" in name:
						lowerCamelCase_					:				Dict              =  "weight_g"
					elif "weight_v" in name:
						lowerCamelCase_					:				str              =  "weight_v"
					elif "bias" in name and "relative_attention_bias" not in name:
						lowerCamelCase_					:				Union[str, Any]              =  "bias"
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						lowerCamelCase_					:				Dict              =  "weight"
					else:
						lowerCamelCase_					:				Optional[Any]              =  None

					set_recursively(a_     ,	a_     ,	a_     ,	a_     ,	a_)
				continue
		if not is_used:
			unused_weights.append(a_)

	logger.warning(F"""Unused weights: {unused_weights}""")







def 							__magic_name__    (     lowerCAmelCase_     ,	lowerCAmelCase_     ,	lowerCAmelCase_     ,	lowerCAmelCase_     ,	lowerCAmelCase_):
	'''simple docstring'''


	lowerCamelCase_					:				Any              =  full_name.split("conv_layers.")[-1]
	lowerCamelCase_					:				int              =  name.split(".")
	lowerCamelCase_					:				str              =  int(items[0])
	lowerCamelCase_					:				Dict              =  int(items[1])

	if type_id == 0:
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
			    F"""{full_name} has size {value.shape}, but"""
			    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
			)
			lowerCamelCase_					:				str              =  value
			logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""")
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
			    F"""{full_name} has size {value.shape}, but"""
			    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
			)
			lowerCamelCase_					:				Optional[Any]              =  value
			logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""")
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
			    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
			    " found."
			)
			lowerCamelCase_					:				int              =  value
			logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""")
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
			    F"""{full_name} has size {value.shape}, but"""
			    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
			)
			lowerCamelCase_					:				List[Any]              =  value
			logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""")
	else:
		unused_weights.append(a_)







@torch.no_grad()
def 							__magic_name__    (     lowerCAmelCase_     ,	lowerCAmelCase_     ,	lowerCAmelCase_=None):
	'''simple docstring'''


	lowerCamelCase_					:				Dict              =  torch.load(a_)
	lowerCamelCase_					:				List[str]              =  WavLMConfigOrig(checkpoint["cfg"])
	lowerCamelCase_					:				str              =  WavLMOrig(a_)
	model.load_state_dict(checkpoint["model"])
	model.eval()

	if config_path is not None:
		lowerCamelCase_					:				str              =  WavLMConfig.from_pretrained(a_)
	else:
		lowerCamelCase_					:				str              =  WavLMConfig()

	lowerCamelCase_					:				Optional[int]              =  WavLMModel(a_)

	recursively_load_weights(a_     ,	a_)

	hf_wavlm.save_pretrained(a_)


if __name__ == "__main__":
							__magic_name__  	=     argparse.ArgumentParser()
							parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
							parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
							parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
							__magic_name__  	=     parser.parse_args()
							convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)







'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class  __lowercase    (	__magic_name__							,				__magic_name__							,				__magic_name__							,				unittest.TestCase			):
   _a    						=   StableUnCLIPImgaImgPipeline
   _a    						=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS
   _a    						=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   _a    						=   frozenset(
       []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
   _a    						=   frozenset([]			)







   def 		UpperCamelCase__      (						self   )					->     List[str]:
     __a								=       32
     __a								=       embedder_hidden_size

     # image encoding components

     __a								=       CLIPImageProcessor(crop_size=32   ,				size=32   )

     torch.manual_seed(0   )
     __a								=       CLIPVisionModelWithProjection(
         CLIPVisionConfig(
             hidden_size=UpperCamelCase   ,				projection_dim=UpperCamelCase   ,				num_hidden_layers=5   ,				num_attention_heads=4   ,				image_size=32   ,				intermediate_size=37   ,				patch_size=1   ,				)   )

     # regular denoising components

     torch.manual_seed(0   )
     __a								=       StableUnCLIPImageNormalizer(embedding_dim=UpperCamelCase   )
     __a								=       DDPMScheduler(beta_schedule='squaredcos_cap_v2'   )

     torch.manual_seed(0   )
     __a								=       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

     torch.manual_seed(0   )
     __a								=       CLIPTextModel(
         CLIPTextConfig(
             bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=UpperCamelCase   ,				projection_dim=32   ,				intermediate_size=37   ,				layer_norm_eps=1e-05   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1000   ,				)   )

     torch.manual_seed(0   )
     __a								=       UNetaDConditionModel(
         sample_size=32   ,				in_channels=4   ,				out_channels=4   ,				down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')   ,				up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')   ,				block_out_channels=(32, 64)   ,				attention_head_dim=(2, 4)   ,				class_embed_type='projection'   ,				projection_class_embeddings_input_dim=embedder_projection_dim * 2   ,				cross_attention_dim=UpperCamelCase   ,				layers_per_block=1   ,				upcast_attention=UpperCamelCase   ,				use_linear_projection=UpperCamelCase   ,				)

     torch.manual_seed(0   )
     __a								=       DDIMScheduler(
         beta_schedule='scaled_linear'   ,				beta_start=0.00_085   ,				beta_end=0.012   ,				prediction_type='v_prediction'   ,				set_alpha_to_one=UpperCamelCase   ,				steps_offset=1   ,				)

     torch.manual_seed(0   )
     __a								=       AutoencoderKL()

     __a								=       {
         # image encoding components
         'feature_extractor': feature_extractor,
         'image_encoder': image_encoder.eval(),
         # image noising components
         'image_normalizer': image_normalizer.eval(),
         'image_noising_scheduler': image_noising_scheduler,
         # regular denoising components
         'tokenizer': tokenizer,
         'text_encoder': text_encoder.eval(),
         'unet': unet.eval(),
         'scheduler': scheduler,
         'vae': vae.eval(),
     }

     return components







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase=0   ,				UpperCamelCase=True   )					->     Dict:
     if str(UpperCamelCase   ).startswith('mps'   ):
       __a								=       torch.manual_seed(UpperCamelCase   )
     else:
       __a								=       torch.Generator(device=UpperCamelCase   ).manual_seed(UpperCamelCase   )

     __a								=       floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(UpperCamelCase   )   ).to(UpperCamelCase   )

     if pil_image:
       __a								=       input_image * 0.5 + 0.5
       __a								=       input_image.clamp(0   ,				1   )
       __a								=       input_image.cpu().permute(0   ,				2   ,				3   ,				1   ).float().numpy()
       __a								=       DiffusionPipeline.numpy_to_pil(UpperCamelCase   )[0]

     return {
         "prompt": "An anime racoon running a marathon",
         "image": input_image,
         "generator": generator,
         "num_inference_steps": 2,
         "output_type": "np",
     }







   @skip_mps
   def 		UpperCamelCase__      (						self   )					->     int:
     __a								=       'cpu'  # ensure determinism for the device-dependent torch.Generator
     __a								=       self.get_dummy_components()
     __a								=       StableUnCLIPImgaImgPipeline(**UpperCamelCase   )
     __a								=       sd_pipe.to(UpperCamelCase   )
     sd_pipe.set_progress_bar_config(disable=UpperCamelCase   )

     __a								=       self.get_dummy_inputs(UpperCamelCase   )
     inputs.update({'image_embeds': None}   )
     __a								=       sd_pipe(**UpperCamelCase   ).images
     __a								=       image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     __a								=       np.array([0.3_872, 0.7_224, 0.5_601, 0.4_741, 0.6_872, 0.5_814, 0.4_636, 0.3_867, 0.5_078]   )

     assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-3







   def 		UpperCamelCase__      (						self   )					->     Any:
     __a								=       torch_device in ['cpu', 'mps']

     self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     __a								=       torch_device in ['cpu', 'mps']

     self._test_inference_batch_single_identical(test_max_difference=UpperCamelCase   )







   @unittest.skipIf(
       torch_device != 'cuda' or not is_xformers_available()   ,				reason='XFormers attention is only available with CUDA and `xformers` installed'   ,				)
   def 		UpperCamelCase__      (						self   )					->     Optional[Any]:
     self._test_xformers_attention_forwardGenerator_pass(test_max_difference=UpperCamelCase   )





@slow
@require_torch_gpu
class  __lowercase    (	unittest.TestCase			):







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()







   def 		UpperCamelCase__      (						self   )					->     str:
     __a								=       load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'   )

     __a								=       load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy'   )

     __a								=       StableUnCLIPImgaImgPipeline.from_pretrained(
         'fusing/stable-unclip-2-1-l-img2img'   ,				torch_dtype=torch.floataa   )
     pipe.to(UpperCamelCase   )
     pipe.set_progress_bar_config(disable=UpperCamelCase   )
     # stable unclip will oom when integration tests are run on a V100,
     # so turn on memory savings
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     __a								=       torch.Generator(device='cpu'   ).manual_seed(0   )
     __a								=       pipe(UpperCamelCase   ,				'anime turle'   ,				generator=UpperCamelCase   ,				output_type='np'   )

     __a								=       output.images[0]

     assert image.shape == (768, 768, 3)

     assert_mean_pixel_difference(UpperCamelCase   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Optional[int]:
     __a								=       load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'   )

     __a								=       load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy'   )

     __a								=       StableUnCLIPImgaImgPipeline.from_pretrained(
         'fusing/stable-unclip-2-1-h-img2img'   ,				torch_dtype=torch.floataa   )
     pipe.to(UpperCamelCase   )
     pipe.set_progress_bar_config(disable=UpperCamelCase   )
     # stable unclip will oom when integration tests are run on a V100,
     # so turn on memory savings
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     __a								=       torch.Generator(device='cpu'   ).manual_seed(0   )
     __a								=       pipe(UpperCamelCase   ,				'anime turle'   ,				generator=UpperCamelCase   ,				output_type='np'   )

     __a								=       output.images[0]

     assert image.shape == (768, 768, 3)

     assert_mean_pixel_difference(UpperCamelCase   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     __a								=       load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'   )

     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     __a								=       StableUnCLIPImgaImgPipeline.from_pretrained(
         'fusing/stable-unclip-2-1-h-img2img'   ,				torch_dtype=torch.floataa   )
     __a								=       pipe.to(UpperCamelCase   )
     pipe.set_progress_bar_config(disable=UpperCamelCase   )
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     __a								=       pipe(
         UpperCamelCase   ,				'anime turtle'   ,				num_inference_steps=2   ,				output_type='np'   ,				)

     __a								=       torch.cuda.max_memory_allocated()
     # make sure that less than 7 GB is allocated
     assert mem_bytes < 7 * 10**9



